This code is for the results to the question, “Is there stability across caregivers, regardless of activity?”, but conducts analyses separately per language and for separate levels for all other-child-centered activities (see pre-registration here: https://osf.io/byjfg/).
library(tidyverse)
library(GGally)
library(ppcor)
library(psych)
library(Hmisc)
library(sjPlot)
# https://github.com/ggobi/ggally/issues/139
my_custom_smooth <- function(data, mapping, ...) {
ggplot(data = data, mapping = mapping) +
geom_point(alpha = .4, color = I("black")) +
geom_smooth(method = "lm", color = I("blue"), ...)
}
# NOTE about periods of non-tCDCS
# gemods refers to when there are designated start/end periods of other-directed speech (ODS); this was captured using gems (@G) using CHAT conventions
# kwalods refers to when ODS was transcribed at an utterance-level within a tCDS activity period between caregiver and child (e.g., other-directed speech in the background); this was captured per utterances using CHAT postcodes
## for tokens/min and types/min, we do not include ODS that occurred within a period of tCDS, because durations were captured by activity and not by utterance
## for mlu, we include all ODS across gemods and kwalods
# NOTE about speech == "all"
# "speech" includes two levels: all, spont
# all = refers to all speech by caregivers
# spont = refers to only speech by caregivers that was considered spontaneous rather than recited (e.g., reading book text, singing memorized common songs like itsy bitsy spider); therefore, 'spont' is a subset of 'all'
# freq
freq <- read_csv("./data_demo_lena_transcripts/freq.csv") %>%
filter(activity != "kwalods",
speech == "all") %>%
mutate(activity = recode(activity, "gemods" = "non_tcds")) %>%
mutate(id = factor(id),
language = factor(language),
activity = factor(activity, levels = c("books", "play", "food",
"routines", "conv", "ac", "non_tcds")))
# mlu
mlu <- read_csv("./data_demo_lena_transcripts/mlu.csv") %>%
filter(speech == "all") %>%
mutate(activity = recode(activity, "ods" = "non_tcds")) %>%
mutate(id = factor(id),
language = factor(language),
activity = factor(activity, levels = c("books", "play", "food",
"routines", "conv", "ac", "non_tcds")))
# chip
# this includes only caregivers, therefore there is no speaker column
# we exclude periods of ODS because this is about responsiveness to the child during periods of tCDS
chip <- read_csv("./data_demo_lena_transcripts/chip.csv") %>%
filter(activity != "ods") %>%
mutate(id = factor(id),
language = factor(language),
activity = factor(activity, levels = c("books", "play", "food",
"routines", "conv", "ac", "non_tcds")))
str(freq)
## spec_tbl_df[,13] [3,308 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ X1 : num [1:3308] 1 2 3 4 5 6 7 8 9 10 ...
## $ id : Factor w/ 90 levels "7292","7352",..: 47 47 47 47 50 50 52 52 52 52 ...
## $ rectime : num [1:3308] 11923 11923 31360 31360 21499 ...
## $ activity : Factor w/ 7 levels "books","play",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ speaker : chr [1:3308] "CHI" "ADULTS" "CHI" "ADULTS" ...
## $ tokens : num [1:3308] 30 151 35 143 58 588 42 286 33 152 ...
## $ types : num [1:3308] 17 70 17 65 17 199 19 53 17 59 ...
## $ segment_num : num [1:3308] 12 12 15 15 2 2 11 11 5 5 ...
## $ language : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
## $ speech : chr [1:3308] "all" "all" "all" "all" ...
## $ dur_min : num [1:3308] 3.55 3.55 6.57 6.57 4.71 ...
## $ tokens_permin: num [1:3308] 8.46 42.57 5.32 21.75 12.31 ...
## $ types_permin : num [1:3308] 4.79 19.73 2.59 9.89 3.61 ...
## - attr(*, "spec")=
## .. cols(
## .. X1 = col_double(),
## .. id = col_double(),
## .. rectime = col_double(),
## .. activity = col_character(),
## .. speaker = col_character(),
## .. tokens = col_double(),
## .. types = col_double(),
## .. segment_num = col_double(),
## .. language = col_character(),
## .. speech = col_character(),
## .. dur_min = col_double(),
## .. tokens_permin = col_double(),
## .. types_permin = col_double()
## .. )
str(mlu)
## spec_tbl_df[,9] [3,002 × 9] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ id : Factor w/ 90 levels "7292","7352",..: 46 46 46 46 46 46 46 46 46 46 ...
## $ activity : Factor w/ 7 levels "books","play",..: 6 6 5 5 7 7 2 2 6 6 ...
## $ speaker : chr [1:3002] "ADULTS" "CHI" "ADULTS" "CHI" ...
## $ segment_num: num [1:3002] 2 2 2 2 2 2 2 2 3 3 ...
## $ words_sum : num [1:3002] 210 66 175 43 11 16 189 47 261 78 ...
## $ num_utt_sum: num [1:3002] 66 35 64 24 2 12 64 28 87 43 ...
## $ mlu_w : num [1:3002] 3.18 1.89 2.73 1.79 5.5 ...
## $ language : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
## $ speech : chr [1:3002] "all" "all" "all" "all" ...
## - attr(*, "spec")=
## .. cols(
## .. id = col_double(),
## .. activity = col_character(),
## .. speaker = col_character(),
## .. segment_num = col_double(),
## .. words_sum = col_double(),
## .. num_utt_sum = col_double(),
## .. mlu_w = col_double(),
## .. language = col_character(),
## .. speech = col_character()
## .. )
str(chip)
## spec_tbl_df[,11] [1,118 × 11] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ activity : Factor w/ 7 levels "books","play",..: 6 5 2 6 5 4 6 5 4 2 ...
## $ id : Factor w/ 90 levels "7292","7352",..: 46 46 46 46 46 46 46 46 46 46 ...
## $ rectime : num [1:1118] 15242 15242 15242 14342 14342 ...
## $ total_adult_utt : num [1:1118] 68 64 65 91 43 13 50 8 65 127 ...
## $ total_child_utt : num [1:1118] 46 34 33 54 17 3 14 1 29 49 ...
## $ total_adult_resp : num [1:1118] 62 51 54 77 24 9 30 4 56 106 ...
## $ total_adult_imitexp : num [1:1118] 18 13 15 25 5 2 9 0 16 21 ...
## $ prop_adultresp_outof_childutt : num [1:1118] 1.35 1.5 1.64 1.43 1.41 ...
## $ prop_adult_imitexp_outof_childutt: num [1:1118] 0.391 0.382 0.455 0.463 0.294 ...
## $ language : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
## $ segment_num : num [1:1118] 2 2 2 3 3 3 4 4 4 5 ...
## - attr(*, "spec")=
## .. cols(
## .. activity = col_character(),
## .. id = col_double(),
## .. rectime = col_double(),
## .. total_adult_utt = col_double(),
## .. total_child_utt = col_double(),
## .. total_adult_resp = col_double(),
## .. total_adult_imitexp = col_double(),
## .. prop_adultresp_outof_childutt = col_double(),
## .. prop_adult_imitexp_outof_childutt = col_double(),
## .. language = col_character(),
## .. segment_num = col_double()
## .. )
# FREQ
freq_adult_en <- freq %>%
filter(speaker == "ADULTS") %>%
filter(language == "english")
freq_adult_sp <- freq %>%
filter(speaker == "ADULTS") %>%
filter(language == "spanish")
# MLU
mlu_adult_en <- mlu %>%
filter(speaker == "ADULTS") %>%
filter(language == "english")
mlu_adult_sp <- mlu %>%
filter(speaker == "ADULTS") %>%
filter(language == "spanish")
# tokens and types: average per activity
freq_adult_per_activity_id_en <- freq_adult_en %>%
group_by(id, activity) %>%
mutate(tokens_permin_avg_act = mean(tokens_permin),
types_permin_avg_act = mean(types_permin)) %>%
distinct(id, activity, language, tokens_permin_avg_act, types_permin_avg_act) %>%
ungroup() %>%
mutate(activity = factor(activity, levels = c("books", "play", "food",
"routines", "conv", "ac", "non_tcds")))
freq_adult_per_activity_id_sp <- freq_adult_sp %>%
group_by(id, activity) %>%
mutate(tokens_permin_avg_act = mean(tokens_permin),
types_permin_avg_act = mean(types_permin)) %>%
distinct(id, activity, language, tokens_permin_avg_act, types_permin_avg_act) %>%
ungroup() %>%
mutate(activity = factor(activity, levels = c("books", "play", "food",
"routines", "conv", "ac", "non_tcds")))
# mlu: average per activity
mlu_adult_per_activity_id_en <- mlu_adult_en %>%
group_by(id, activity) %>%
mutate(mluw_avg_act = mean(mlu_w)) %>%
distinct(id, activity, language, mluw_avg_act) %>%
ungroup() %>%
mutate(activity = factor(activity, levels = c("books", "play", "food",
"routines", "conv", "ac", "non_tcds")))
mlu_adult_per_activity_id_sp <- mlu_adult_sp %>%
group_by(id, activity) %>%
mutate(mluw_avg_act = mean(mlu_w)) %>%
distinct(id, activity, language, mluw_avg_act) %>%
ungroup() %>%
mutate(activity = factor(activity, levels = c("books", "play", "food",
"routines", "conv", "ac", "non_tcds")))
# chip: average per activity
chip_per_activity_id_en <- chip %>%
filter(language == "english") %>%
group_by(id, activity) %>%
mutate(prop_adultresp_avg_act = mean(prop_adultresp_outof_childutt, na.rm = T),
prop_adult_imitexp_avg_act = mean(prop_adult_imitexp_outof_childutt, na.rm = T)) %>%
distinct(id, activity, language, prop_adultresp_avg_act, prop_adult_imitexp_avg_act)
chip_per_activity_id_sp <- chip %>%
filter(language == "spanish") %>%
group_by(id, activity) %>%
mutate(prop_adultresp_avg_act = mean(prop_adultresp_outof_childutt, na.rm = T),
prop_adult_imitexp_avg_act = mean(prop_adult_imitexp_outof_childutt, na.rm = T)) %>%
distinct(id, activity, language, prop_adultresp_avg_act, prop_adult_imitexp_avg_act)
# tokens
# all
tokens_mtx_rate_en <- freq_adult_per_activity_id_en %>%
dplyr::select(id, language, activity, tokens_permin_avg_act) %>%
pivot_wider(names_from = activity, values_from = tokens_permin_avg_act) %>%
ungroup() %>%
dplyr::select(c("books", "play", "food", "routines", "conv", "ac", "non_tcds"))
tokens_mtx_rate_sp <- freq_adult_per_activity_id_sp %>%
dplyr::select(id, language, activity, tokens_permin_avg_act) %>%
pivot_wider(names_from = activity, values_from = tokens_permin_avg_act) %>%
ungroup() %>%
dplyr::select(c("books", "play", "food", "routines", "conv", "ac", "non_tcds"))
# types
# all
types_mtx_rate_en <- freq_adult_per_activity_id_en %>%
dplyr::select(id, language, activity, types_permin_avg_act) %>%
pivot_wider(names_from = activity, values_from = types_permin_avg_act) %>%
ungroup() %>%
dplyr::select(c("books", "play", "food", "routines", "conv", "ac", "non_tcds"))
types_mtx_rate_sp <- freq_adult_per_activity_id_sp %>%
dplyr::select(id, language, activity, types_permin_avg_act) %>%
pivot_wider(names_from = activity, values_from = types_permin_avg_act) %>%
ungroup() %>%
dplyr::select(c("books", "play", "food", "routines", "conv", "ac", "non_tcds"))
# all
mlu_mtx_en <- mlu_adult_per_activity_id_en %>%
dplyr::select(id, language, activity, mluw_avg_act) %>%
pivot_wider(names_from = activity, values_from = mluw_avg_act) %>%
ungroup() %>%
dplyr::select(c("books", "play", "food", "routines", "conv", "ac", "non_tcds"))
mlu_mtx_sp <- mlu_adult_per_activity_id_sp %>%
dplyr::select(id, language, activity, mluw_avg_act) %>%
pivot_wider(names_from = activity, values_from = mluw_avg_act) %>%
ungroup() %>%
dplyr::select(c("books", "play", "food", "routines", "conv", "ac", "non_tcds"))
# prop responses
propresp_mtx_en <- chip_per_activity_id_en %>%
dplyr::select(id, language, activity, prop_adultresp_avg_act) %>%
pivot_wider(names_from = activity, values_from = prop_adultresp_avg_act) %>%
ungroup() %>%
dplyr::select(c("books", "play", "food", "routines", "conv", "ac"))
propresp_mtx_sp <- chip_per_activity_id_sp %>%
dplyr::select(id, language, activity, prop_adultresp_avg_act) %>%
pivot_wider(names_from = activity, values_from = prop_adultresp_avg_act) %>%
ungroup() %>%
dplyr::select(c("books", "play", "food", "routines", "conv", "ac"))
# prop imitations and expansions
propimitexp_mtx_en <- chip_per_activity_id_en %>%
dplyr::select(id, language, activity, prop_adult_imitexp_avg_act) %>%
pivot_wider(names_from = activity, values_from = prop_adult_imitexp_avg_act) %>%
ungroup() %>%
dplyr::select(c("books", "play", "food", "routines", "conv", "ac"))
propimitexp_mtx_sp <- chip_per_activity_id_sp %>%
dplyr::select(id, language, activity, prop_adult_imitexp_avg_act) %>%
pivot_wider(names_from = activity, values_from = prop_adult_imitexp_avg_act) %>%
ungroup() %>%
dplyr::select(c("books", "play", "food", "routines", "conv", "ac"))
# english
ggpairs(data = tokens_mtx_rate_en,
columns = 1:7,
switch = 'y',
lower = list(continuous = my_custom_smooth),
upper = list(continuous = wrap("cor", size = 7)),
title = "English - Tokens rate") +
theme_classic() +
theme(text= element_text(size = 18),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", size = 15),
strip.text.x = element_text(face = "bold", size = 15))
# correlation matrices
rcorr(as.matrix(tokens_mtx_rate_en), type = c("pearson"))
## books play food routines conv ac non_tcds
## books 1.00 0.50 0.28 0.29 0.47 0.25 0.38
## play 0.50 1.00 0.13 0.38 0.45 0.32 0.50
## food 0.28 0.13 1.00 -0.19 0.12 0.05 0.02
## routines 0.29 0.38 -0.19 1.00 0.29 0.05 0.40
## conv 0.47 0.45 0.12 0.29 1.00 0.16 0.46
## ac 0.25 0.32 0.05 0.05 0.16 1.00 0.14
## non_tcds 0.38 0.50 0.02 0.40 0.46 0.14 1.00
##
## n
## books play food routines conv ac non_tcds
## books 22 19 15 14 21 22 22
## play 19 39 27 27 37 39 39
## food 15 27 31 22 30 31 31
## routines 14 27 22 32 30 32 32
## conv 21 37 30 30 43 43 43
## ac 22 39 31 32 43 45 45
## non_tcds 22 39 31 32 43 45 45
##
## P
## books play food routines conv ac non_tcds
## books 0.0299 0.3132 0.3169 0.0323 0.2553 0.0838
## play 0.0299 0.5110 0.0524 0.0054 0.0499 0.0012
## food 0.3132 0.5110 0.3871 0.5188 0.7813 0.9357
## routines 0.3169 0.0524 0.3871 0.1261 0.7711 0.0244
## conv 0.0323 0.0054 0.5188 0.1261 0.3047 0.0018
## ac 0.2553 0.0499 0.7813 0.7711 0.3047 0.3659
## non_tcds 0.0838 0.0012 0.9357 0.0244 0.0018 0.3659
# spanish
ggpairs(data = tokens_mtx_rate_sp,
columns = 1:7,
switch = 'y',
lower = list(continuous = my_custom_smooth),
upper = list(continuous = wrap("cor", size = 7)),
title = "Spanish - Tokens rate") +
theme_classic() +
theme(text= element_text(size = 18),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", size = 15),
strip.text.x = element_text(face = "bold", size = 15))
# correlation matrices
rcorr(as.matrix(tokens_mtx_rate_sp), type = c("pearson"))
## books play food routines conv ac non_tcds
## books 1.00 0.58 -0.21 0.72 0.61 -0.05 0.35
## play 0.58 1.00 0.14 0.18 0.53 0.23 0.25
## food -0.21 0.14 1.00 0.12 -0.01 0.16 0.19
## routines 0.72 0.18 0.12 1.00 0.52 0.43 0.51
## conv 0.61 0.53 -0.01 0.52 1.00 0.29 0.47
## ac -0.05 0.23 0.16 0.43 0.29 1.00 0.43
## non_tcds 0.35 0.25 0.19 0.51 0.47 0.43 1.00
##
## n
## books play food routines conv ac non_tcds
## books 20 15 11 15 20 20 20
## play 15 37 26 29 36 37 37
## food 11 26 31 24 29 31 31
## routines 15 29 24 35 35 35 35
## conv 20 36 29 35 43 43 43
## ac 20 37 31 35 43 45 45
## non_tcds 20 37 31 35 43 45 45
##
## P
## books play food routines conv ac non_tcds
## books 0.0236 0.5356 0.0025 0.0040 0.8235 0.1327
## play 0.0236 0.5001 0.3461 0.0009 0.1663 0.1315
## food 0.5356 0.5001 0.5896 0.9702 0.3956 0.2975
## routines 0.0025 0.3461 0.5896 0.0013 0.0104 0.0016
## conv 0.0040 0.0009 0.9702 0.0013 0.0632 0.0017
## ac 0.8235 0.1663 0.3956 0.0104 0.0632 0.0034
## non_tcds 0.1327 0.1315 0.2975 0.0016 0.0017 0.0034
# english
ggpairs(data = types_mtx_rate_en,
columns = 1:7,
switch = 'y',
lower = list(continuous = my_custom_smooth),
upper = list(continuous = wrap("cor", size = 7)),
title = "English - Types rate") +
theme_classic() +
theme(text= element_text(size = 18),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", size = 15),
strip.text.x = element_text(face = "bold", size = 15))
# correlation matrices
rcorr(as.matrix(types_mtx_rate_en), type = c("pearson"))
## books play food routines conv ac non_tcds
## books 1.00 0.35 -0.22 0.40 0.25 0.14 0.02
## play 0.35 1.00 -0.07 -0.02 0.19 0.22 0.34
## food -0.22 -0.07 1.00 -0.12 0.13 0.11 -0.03
## routines 0.40 -0.02 -0.12 1.00 -0.02 0.00 0.10
## conv 0.25 0.19 0.13 -0.02 1.00 0.01 0.37
## ac 0.14 0.22 0.11 0.00 0.01 1.00 0.17
## non_tcds 0.02 0.34 -0.03 0.10 0.37 0.17 1.00
##
## n
## books play food routines conv ac non_tcds
## books 22 19 15 14 21 22 22
## play 19 39 27 27 37 39 39
## food 15 27 31 22 30 31 31
## routines 14 27 22 32 30 32 32
## conv 21 37 30 30 43 43 43
## ac 22 39 31 32 43 45 45
## non_tcds 22 39 31 32 43 45 45
##
## P
## books play food routines conv ac non_tcds
## books 0.1473 0.4409 0.1537 0.2669 0.5270 0.9463
## play 0.1473 0.7287 0.9017 0.2520 0.1885 0.0315
## food 0.4409 0.7287 0.5926 0.4843 0.5418 0.8581
## routines 0.1537 0.9017 0.5926 0.9035 0.9976 0.5857
## conv 0.2669 0.2520 0.4843 0.9035 0.9523 0.0136
## ac 0.5270 0.1885 0.5418 0.9976 0.9523 0.2589
## non_tcds 0.9463 0.0315 0.8581 0.5857 0.0136 0.2589
# spanish
ggpairs(data = types_mtx_rate_sp,
columns = 1:7,
switch = 'y',
lower = list(continuous = my_custom_smooth),
upper = list(continuous = wrap("cor", size = 7)),
title = "Spanish - Types rate") +
theme_classic() +
theme(text= element_text(size = 18),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", size = 15),
strip.text.x = element_text(face = "bold", size = 15))
# correlation matrices
rcorr(as.matrix(types_mtx_rate_sp), type = c("pearson"))
## books play food routines conv ac non_tcds
## books 1.00 0.37 -0.27 0.34 0.34 -0.22 0.01
## play 0.37 1.00 0.47 0.04 0.41 0.10 0.18
## food -0.27 0.47 1.00 0.14 0.10 0.05 0.29
## routines 0.34 0.04 0.14 1.00 0.11 0.00 0.12
## conv 0.34 0.41 0.10 0.11 1.00 0.05 0.47
## ac -0.22 0.10 0.05 0.00 0.05 1.00 0.22
## non_tcds 0.01 0.18 0.29 0.12 0.47 0.22 1.00
##
## n
## books play food routines conv ac non_tcds
## books 20 15 11 15 20 20 20
## play 15 37 26 29 36 37 37
## food 11 26 31 24 29 31 31
## routines 15 29 24 35 35 35 35
## conv 20 36 29 35 43 43 43
## ac 20 37 31 35 43 45 45
## non_tcds 20 37 31 35 43 45 45
##
## P
## books play food routines conv ac non_tcds
## books 0.1787 0.4281 0.2174 0.1426 0.3414 0.9587
## play 0.1787 0.0165 0.8523 0.0127 0.5708 0.2926
## food 0.4281 0.0165 0.5153 0.6012 0.7788 0.1105
## routines 0.2174 0.8523 0.5153 0.5313 0.9922 0.4758
## conv 0.1426 0.0127 0.6012 0.5313 0.7391 0.0014
## ac 0.3414 0.5708 0.7788 0.9922 0.7391 0.1528
## non_tcds 0.9587 0.2926 0.1105 0.4758 0.0014 0.1528
# english
ggpairs(data = mlu_mtx_en,
columns = 1:7,
switch = 'y',
lower = list(continuous = my_custom_smooth),
upper = list(continuous = wrap("cor", size = 11)),
title = "English - MLUw") +
theme_classic() +
theme(text= element_text(size = 26),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", size = 20),
strip.text.x = element_text(face = "bold", size = 20))
# correlation matrices
rcorr(as.matrix(mlu_mtx_en), type = c("pearson"))
## books play food routines conv ac non_tcds
## books 1.00 0.35 0.55 -0.02 0.50 0.21 0.00
## play 0.35 1.00 0.54 0.32 0.42 0.32 0.33
## food 0.55 0.54 1.00 0.56 0.68 0.53 0.56
## routines -0.02 0.32 0.56 1.00 0.28 0.20 0.40
## conv 0.50 0.42 0.68 0.28 1.00 0.50 0.34
## ac 0.21 0.32 0.53 0.20 0.50 1.00 0.18
## non_tcds 0.00 0.33 0.56 0.40 0.34 0.18 1.00
##
## n
## books play food routines conv ac non_tcds
## books 22 19 15 14 21 22 22
## play 19 39 27 27 37 39 39
## food 15 27 31 22 30 31 31
## routines 14 27 22 32 30 32 32
## conv 21 37 30 30 43 43 43
## ac 22 39 31 32 43 45 45
## non_tcds 22 39 31 32 43 45 45
##
## P
## books play food routines conv ac non_tcds
## books 0.1364 0.0338 0.9410 0.0200 0.3399 0.9945
## play 0.1364 0.0036 0.1046 0.0102 0.0473 0.0431
## food 0.0338 0.0036 0.0069 0.0000 0.0020 0.0011
## routines 0.9410 0.1046 0.0069 0.1347 0.2690 0.0221
## conv 0.0200 0.0102 0.0000 0.1347 0.0006 0.0254
## ac 0.3399 0.0473 0.0020 0.2690 0.0006 0.2386
## non_tcds 0.9945 0.0431 0.0011 0.0221 0.0254 0.2386
# spanish
ggpairs(data = mlu_mtx_sp,
columns = 1:7,
switch = 'y',
lower = list(continuous = my_custom_smooth),
upper = list(continuous = wrap("cor", size = 11)),
title = "Spanish - MLUw") +
theme_classic() +
theme(text= element_text(size = 26),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", size = 20),
strip.text.x = element_text(face = "bold", size = 20))
# correlation matrices
rcorr(as.matrix(mlu_mtx_sp), type = c("pearson"))
## books play food routines conv ac non_tcds
## books 1.00 0.66 -0.23 0.42 0.54 0.37 0.48
## play 0.66 1.00 0.29 0.25 0.54 0.41 0.47
## food -0.23 0.29 1.00 0.25 0.14 0.36 0.25
## routines 0.42 0.25 0.25 1.00 0.49 0.70 0.47
## conv 0.54 0.54 0.14 0.49 1.00 0.52 0.32
## ac 0.37 0.41 0.36 0.70 0.52 1.00 0.45
## non_tcds 0.48 0.47 0.25 0.47 0.32 0.45 1.00
##
## n
## books play food routines conv ac non_tcds
## books 20 15 11 15 20 20 20
## play 15 37 26 29 36 37 37
## food 11 26 31 24 29 31 31
## routines 15 29 24 35 35 35 35
## conv 20 36 29 35 43 43 43
## ac 20 37 31 35 43 45 45
## non_tcds 20 37 31 35 43 45 45
##
## P
## books play food routines conv ac non_tcds
## books 0.0079 0.4894 0.1228 0.0139 0.1105 0.0333
## play 0.0079 0.1490 0.1852 0.0006 0.0108 0.0031
## food 0.4894 0.1490 0.2335 0.4586 0.0460 0.1735
## routines 0.1228 0.1852 0.2335 0.0031 0.0000 0.0047
## conv 0.0139 0.0006 0.4586 0.0031 0.0003 0.0355
## ac 0.1105 0.0108 0.0460 0.0000 0.0003 0.0017
## non_tcds 0.0333 0.0031 0.1735 0.0047 0.0355 0.0017
# english
ggpairs(data = propresp_mtx_en,
columns = 1:6,
switch = 'y',
lower = list(continuous = my_custom_smooth),
upper = list(continuous = wrap("cor", size = 7)),
title = "English - Prop Resp") +
theme_classic() +
theme(text= element_text(size = 18),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", size = 15),
strip.text.x = element_text(face = "bold", size = 15))
# correlation matrices
rcorr(as.matrix(propresp_mtx_en), type = c("pearson"))
## books play food routines conv ac
## books 1.00 0.50 0.36 0.03 0.34 0.29
## play 0.50 1.00 0.23 0.28 0.17 -0.14
## food 0.36 0.23 1.00 -0.23 0.04 0.05
## routines 0.03 0.28 -0.23 1.00 0.55 -0.05
## conv 0.34 0.17 0.04 0.55 1.00 -0.05
## ac 0.29 -0.14 0.05 -0.05 -0.05 1.00
##
## n
## books play food routines conv ac
## books 22 19 15 12 21 20
## play 19 39 26 26 36 38
## food 15 26 30 20 28 28
## routines 12 26 20 29 27 27
## conv 21 36 28 27 41 39
## ac 20 38 28 27 39 43
##
## P
## books play food routines conv ac
## books 0.0292 0.1871 0.9285 0.1260 0.2094
## play 0.0292 0.2540 0.1705 0.3234 0.3899
## food 0.1871 0.2540 0.3265 0.8262 0.7937
## routines 0.9285 0.1705 0.3265 0.0033 0.8048
## conv 0.1260 0.3234 0.8262 0.0033 0.7845
## ac 0.2094 0.3899 0.7937 0.8048 0.7845
# spanish
ggpairs(data = propresp_mtx_sp,
columns = 1:6,
switch = 'y',
lower = list(continuous = my_custom_smooth),
upper = list(continuous = wrap("cor", size = 7)),
title = "Spanish - Prop Resp") +
theme_classic() +
theme(text= element_text(size = 18),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", size = 15),
strip.text.x = element_text(face = "bold", size = 15))
# correlation matrices
rcorr(as.matrix(propresp_mtx_sp), type = c("pearson"))
## books play food routines conv ac
## books 1.00 0.21 -0.31 0.42 0.41 0.03
## play 0.21 1.00 0.40 -0.04 0.23 0.37
## food -0.31 0.40 1.00 0.62 0.35 0.43
## routines 0.42 -0.04 0.62 1.00 0.25 0.14
## conv 0.41 0.23 0.35 0.25 1.00 0.23
## ac 0.03 0.37 0.43 0.14 0.23 1.00
##
## n
## books play food routines conv ac
## books 20 15 10 15 19 19
## play 15 36 24 28 33 35
## food 10 24 30 23 27 29
## routines 15 28 23 35 35 34
## conv 19 33 27 35 41 40
## ac 19 35 29 34 40 44
##
## P
## books play food routines conv ac
## books 0.4630 0.3912 0.1211 0.0851 0.9106
## play 0.4630 0.0523 0.8211 0.1893 0.0269
## food 0.3912 0.0523 0.0015 0.0747 0.0193
## routines 0.1211 0.8211 0.0015 0.1418 0.4344
## conv 0.0851 0.1893 0.0747 0.1418 0.1602
## ac 0.9106 0.0269 0.0193 0.4344 0.1602
# english
ggpairs(data = propimitexp_mtx_en,
columns = 1:6,
switch = 'y',
lower = list(continuous = my_custom_smooth),
upper = list(continuous = wrap("cor", size = 7)),
title = "English - Prop Imit/Exp") +
theme_classic() +
theme(text= element_text(size = 18),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", size = 15),
strip.text.x = element_text(face = "bold", size = 15))
# correlation matrices
rcorr(as.matrix(propimitexp_mtx_en), type = c("pearson"))
## books play food routines conv ac
## books 1.00 0.44 0.02 -0.05 0.40 0.09
## play 0.44 1.00 0.03 0.34 0.39 0.06
## food 0.02 0.03 1.00 -0.23 -0.06 -0.01
## routines -0.05 0.34 -0.23 1.00 0.47 0.16
## conv 0.40 0.39 -0.06 0.47 1.00 0.03
## ac 0.09 0.06 -0.01 0.16 0.03 1.00
##
## n
## books play food routines conv ac
## books 22 19 15 12 21 20
## play 19 39 26 26 36 38
## food 15 26 30 20 28 28
## routines 12 26 20 29 27 27
## conv 21 36 28 27 41 39
## ac 20 38 28 27 39 43
##
## P
## books play food routines conv ac
## books 0.0606 0.9369 0.8760 0.0738 0.7026
## play 0.0606 0.8972 0.0849 0.0196 0.7260
## food 0.9369 0.8972 0.3339 0.7604 0.9604
## routines 0.8760 0.0849 0.3339 0.0126 0.4247
## conv 0.0738 0.0196 0.7604 0.0126 0.8374
## ac 0.7026 0.7260 0.9604 0.4247 0.8374
# spanish
ggpairs(data = propimitexp_mtx_en,
columns = 1:6,
switch = 'y',
lower = list(continuous = my_custom_smooth),
upper = list(continuous = wrap("cor", size = 7)),
title = "Spanish - Prop Imit/Exp") +
theme_classic() +
theme(text= element_text(size = 18),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", size = 15),
strip.text.x = element_text(face = "bold", size = 15))
# correlation matrices
rcorr(as.matrix(propimitexp_mtx_sp), type = c("pearson"))
## books play food routines conv ac
## books 1.00 0.32 -0.48 0.48 0.09 -0.09
## play 0.32 1.00 0.39 0.09 0.43 0.32
## food -0.48 0.39 1.00 0.11 0.38 0.40
## routines 0.48 0.09 0.11 1.00 0.24 0.20
## conv 0.09 0.43 0.38 0.24 1.00 0.26
## ac -0.09 0.32 0.40 0.20 0.26 1.00
##
## n
## books play food routines conv ac
## books 20 15 10 15 19 19
## play 15 36 24 28 33 35
## food 10 24 30 23 27 29
## routines 15 28 23 35 35 34
## conv 19 33 27 35 41 40
## ac 19 35 29 34 40 44
##
## P
## books play food routines conv ac
## books 0.2419 0.1630 0.0681 0.7109 0.7044
## play 0.2419 0.0579 0.6506 0.0136 0.0640
## food 0.1630 0.0579 0.6231 0.0518 0.0303
## routines 0.0681 0.6506 0.6231 0.1705 0.2450
## conv 0.7109 0.0136 0.0518 0.1705 0.0985
## ac 0.7044 0.0640 0.0303 0.2450 0.0985